Physical Environment Integrity Monitoring
Objective
Continuously verify the integrity and reliability of physical environment sensor data on which AI decisions are based, preventing AI actions grounded in corrupted, degraded, or spoofed environmental inputs.
Control / requirement
Sensor integrity monitoring covering: (1) Hardware health — sensor self-test results, calibration drift indicators, environmental exposure limits. Alert when sensor confidence falls below threshold. (2) Data plausibility — real-time statistical validation against physical laws, historical baselines, and redundant sensor cross-validation. (3) Degraded sensor handling — explicit policy for each sensor failure mode: degrade gracefully (reduce AI authority, increase human oversight) or enter safe state. (4) Calibration management — automated alert when calibration certificates expire; block AI system from operational use with expired sensor calibration.
Business impact
AI systems operating on degraded or spoofed sensor data take actions calibrated to a false model of physical reality. In automotive, aviation, or industrial contexts, sensor integrity failures are a primary cause of catastrophic incidents. ISO 26262 and IEC 61508 both require sensor integrity management as a mandatory element of functional safety certification.
Validation approach
Test ID: D9-CTL-05-VTS-001 | Test Type: Hybrid (Hardware-in-the-Loop + Manual) | Harness: pytest tests/d9_physical_ai/test_physical_env_integrity.py -v # oda3-gaissf-vts | Pass Criteria: all sensor failure modes produce defined system response within 500ms in 100% of injected failure tests; calibration_compliance_rate = 100%. Test: inject each sensor failure mode (disconnect, out-of-range, stuck value, noise injection, drift) and verify response. Independent assessor verifies degraded mode is operationally safe.
Expected evidence
Not separately specified in the available source.
Mapping and source
Not separately specified in the available source.
Implementation guidance
Translate the requirement into system-specific procedures, responsible roles, technical configurations and review conditions. Retain evidence showing both design and operating performance. Where the source provides no separate implementation instruction, do not infer that a single technical mechanism is sufficient.
Assessment considerations
- Confirm scope and applicability.
- Inspect control design and responsible ownership.
- Test representative operation and adverse conditions where appropriate.
- Evaluate evidence provenance, completeness and contradictory evidence.
- Record limitations and notably absent outcomes.